> What exactly is the problem with big rows? During compaction the row will be passed through a slower two pass processing, this add's to IO pressure. Counting big rows requires that the entire row be read. Repairing big rows requires that the entire row be repaired.
I generally avoid rows above a few 10's of MB as they result in more memory churn and create admin problems as above. > What exactly is the problem with big rows? > And, how can we should place our data in this case (see the schema in > the previous replies)? Splitting one report to multiple rows is > uncomfortably :-( Looking at your row sizes below, the question is "How do I store an object which may be up to 3.5GB in size." AFAIK there are no hard limits that would prevent you putting that in one row. And avoiding super columns may save some space. You could have a Simple CF, where the each report is one row, each report row is one column and the report row is serialised (with JSON or protobufs etc) and stored in the column value. But i would recommend creating a model where row size is constrained in space. E.g. Report CF: * one report per row. * one column per report row * column value is empty. Report Rows CF: * one row per 100 report rows, e.g. <report_id : first_row_number> * column name is report row number. * column value is report data (Or use composite column names, e.g. <row_number : report_column> You can still do ranges, buy you have to do some client side work to work it out. Hope that helps. ----------------- Aaron Morton Freelance Developer @aaronmorton http://www.thelastpickle.com On 24/09/2012, at 5:14 PM, Denis Gabaydulin <gaba...@gmail.com> wrote: > On Sun, Sep 23, 2012 at 10:41 PM, aaron morton <aa...@thelastpickle.com> > wrote: >> /var/log/cassandra$ cat system.log | grep "Compacting large" | grep -E >> "[0-9]+ bytes" -o | cut -d " " -f 1 | awk '{ foo = $1 / 1024 / 1024 ; >> print foo "MB" }' | sort -nr | head -n 50 >> >> >> Is it bad signal? >> >> Sorry, I do not know what this is outputting. >> > > This is outputting size of big rows which cassandra had compacted before. > >> As I can see in cfstats, compacted row maximum size: 386857368 ! >> >> Yes. >> Having rows in the 100's of MB is will cause problems. Doubly so if they are >> large super columns. >> > > What exactly is the problem with big rows? > And, how can we should place our data in this case (see the schema in > the previous replies)? Splitting one report to multiple rows is > uncomfortably :-( > > >> Cheers >> >> >> >> ----------------- >> Aaron Morton >> Freelance Developer >> @aaronmorton >> http://www.thelastpickle.com >> >> On 22/09/2012, at 5:07 AM, Denis Gabaydulin <gaba...@gmail.com> wrote: >> >> And some stuff from log: >> >> >> /var/log/cassandra$ cat system.log | grep "Compacting large" | grep -E >> "[0-9]+ bytes" -o | cut -d " " -f 1 | awk '{ foo = $1 / 1024 / 1024 ; >> print foo "MB" }' | sort -nr | head -n 50 >> 3821.55MB >> 3337.85MB >> 1221.64MB >> 1128.67MB >> 930.666MB >> 916.4MB >> 861.114MB >> 843.325MB >> 711.813MB >> 706.992MB >> 674.282MB >> 673.861MB >> 658.305MB >> 557.756MB >> 531.577MB >> 493.112MB >> 492.513MB >> 492.291MB >> 484.484MB >> 479.908MB >> 465.742MB >> 464.015MB >> 459.95MB >> 454.472MB >> 441.248MB >> 428.763MB >> 424.028MB >> 416.663MB >> 416.191MB >> 409.341MB >> 406.895MB >> 397.314MB >> 388.27MB >> 376.714MB >> 371.298MB >> 368.819MB >> 366.92MB >> 361.371MB >> 360.509MB >> 356.168MB >> 355.012MB >> 354.897MB >> 354.759MB >> 347.986MB >> 344.109MB >> 335.546MB >> 329.529MB >> 326.857MB >> 326.252MB >> 326.237MB >> >> Is it bad signal? >> >> On Fri, Sep 21, 2012 at 8:22 PM, Denis Gabaydulin <gaba...@gmail.com> wrote: >> >> Found one more intersting fact. >> As I can see in cfstats, compacted row maximum size: 386857368 ! >> >> On Fri, Sep 21, 2012 at 12:50 PM, Denis Gabaydulin <gaba...@gmail.com> >> wrote: >> >> Reports - is a SuperColumnFamily >> >> Each report has unique identifier (report_id). This is a key of >> SuperColumnFamily. >> And a report saved in separate row. >> >> A report is consisted of report rows (may vary between 1 and 500000, >> but most are small). >> >> Each report row is saved in separate super column. Hector based code: >> >> superCfMutator.addInsertion( >> report_id, >> "Reports", >> HFactory.createSuperColumn( >> report_row_id, >> mapper.convertObject(object), >> columnDefinition.getTopSerializer(), >> columnDefinition.getSubSerializer(), >> inferringSerializer >> ) >> ); >> >> We have two frequent operation: >> >> 1. count report rows by report_id (calculate number of super columns >> in the row). >> 2. get report rows by report_id and range predicate (get super columns >> from the row with range predicate). >> >> I can't see here a big super columns :-( >> >> On Fri, Sep 21, 2012 at 3:10 AM, Tyler Hobbs <ty...@datastax.com> wrote: >> >> I'm not 100% that I understand your data model and read patterns correctly, >> but it sounds like you have large supercolumns and are requesting some of >> the subcolumns from individual super columns. If that's the case, the issue >> is that Cassandra must deserialize the entire supercolumn in memory whenever >> you read *any* of the subcolumns. This is one of the reasons why composite >> columns are recommended over supercolumns. >> >> >> On Thu, Sep 20, 2012 at 6:45 AM, Denis Gabaydulin <gaba...@gmail.com> wrote: >> >> >> p.s. Cassandra 1.1.4 >> >> On Thu, Sep 20, 2012 at 3:27 PM, Denis Gabaydulin <gaba...@gmail.com> >> wrote: >> >> Hi, all! >> >> We have a cluster with virtual 7 nodes (disk storage is connected to >> nodes with iSCSI). The storage schema is: >> >> Reports:{ >> 1:{ >> 1:{"value1":"some val", "value2":"some val"}, >> 2:{"value1":"some val", "value2":"some val"} >> ... >> }, >> 2:{ >> 1:{"value1":"some val", "value2":"some val"}, >> 2:{"value1":"some val", "value2":"some val"} >> ... >> } >> ... >> } >> >> create keyspace osmp_reports >> with placement_strategy = 'SimpleStrategy' >> and strategy_options = {replication_factor : 4} >> and durable_writes = true; >> >> use osmp_reports; >> >> create column family QueryReportResult >> with column_type = 'Super' >> and comparator = 'BytesType' >> and subcomparator = 'BytesType' >> and default_validation_class = 'BytesType' >> and key_validation_class = 'BytesType' >> and read_repair_chance = 1.0 >> and dclocal_read_repair_chance = 0.0 >> and gc_grace = 432000 >> and min_compaction_threshold = 4 >> and max_compaction_threshold = 32 >> and replicate_on_write = true >> and compaction_strategy = >> 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy' >> and caching = 'KEYS_ONLY'; >> >> ============================================= >> >> Read/Write CL: 2 >> >> Most of the reports are small, but some of them could have a half >> mullion of rows (xml). Typical operations on this dataset is: >> >> count report rows by report_id (top level id of super column); >> get columns (report_rows) by range predicate and limit for given >> report_id. >> >> A data is written once and hasn't never been updated. >> >> So, time to time a couple of nodes crashes with OOM exception. Heap >> dump says, that we have a lot of super columns in memory. >> For example, I see one of the reports is in memory entirely. How it >> could be possible? If we don't load the whole report, cassandra could >> whether do this for some internal reasons? >> >> What should we do to avoid OOMs? >> >> >> >> >> >> -- >> Tyler Hobbs >> DataStax >> >>
